Noise reduction is an important factor in pre-encoding video preprocessing. It is based on recursive time domain filtering, for which the recursion rate is adjusted according to the noise level present in the sequence of images to be processed.
Estimation of the noise level is therefore important for obtaining a filtering system which adapts automatically to the sequence of incoming images.
In known methods, in the total absence of movement and because of its time-random nature, the noise can be detected by establishing the pixel-by-pixel difference between two consecutive images. The average of the differences in an image then constitutes a representation of the noise level.
When there is movement, the noise level can be estimated by calculating the difference between the current image and the preceding image reconstructed to match the current image using the vector fields supplied by the movement estimator.
However, such methods present numerous drawbacks:                the movement vector can produce estimation errors, provoking a poor reconstruction of the image and therefore an overestimation of the noise level, since there is movement in the image sequence,        the movement vector and therefore the differences between images (Displaced Frame Difference DFD), are inconsistent all around the perimeter of the image, which also falsifies the estimation of the noise level,                    the estimation of the noise level is based on the random nature of the noise, which is not always reliable.                        